341 research outputs found
Semiparametric estimation of the proportional rates model for recurrent events data with missing event category
Proportional rates models are frequently used for the analysis of recurrent event data with multiple event categories. When some of the event categories are missing, a conventional approach is to either exclude the missing data for a complete-case analysis or employ a parametric model for the missing event type. It is well known that the complete-case analysis is inconsistent when the missingness depends on covariates, and the parametric approach may incur bias when the model is misspecified. In this paper, we aim to provide a more robust approach using a rate proportion method for the imputation of missing event types. We show that the log-odds of the event type can be written as a semiparametric generalized linear model, facilitating a theoretically justified estimation framework. Comprehensive simulation studies were conducted demonstrating the improved performance of the semiparametric method over parametric procedures. Multiple types of Pseudomonas aeruginosa infections of young cystic fibrosis patients were analyzed to demonstrate the feasibility of our proposed approach
Semiparametric additive marginal regression models for multiple type recurrent events
Recurrent event data are often encountered in biomedical research, for example, recurrent infections or recurrent hospitalizations for patients after renal transplant. In many studies, there are more than one type of events of interest. Cai and Schaubel (2004) advocated a proportional marginal rate model for multiple type recurrent event data. In this paper, we propose a general additive marginal rate regression model. Estimating equations approach is used to obtain the estimators of regression coefficients and baseline rate function. We prove the consistency and asymptotic normality of the proposed estimators. The finite sample properties of our estimators are demonstrated by simulations. The proposed methods are applied to the India renal transplant study to examine risk factors for bacterial, fungal and viral infections
Statistical methods for recurrent event data in the presence of a terminal event and incomplete covariate information
In many clinical and epidemiological studies, recurrent events such as infections in immunocompromised patients or injuries in athletes often occur. It is of interest to examine the relationship between covariates and recurrent events, however in many situations, some of the covariates collected involve missing information due to various reasons. Under such missingness, a commonly practiced method is to analyze complete cases; this method may be inefficient or result in biased estimates for parameters. In this dissertation, we develop methods to analyze recurrent events data with missing covariate information. These will be useful in reducing the bias and improving the efficiency of parameter estimates. This method is motivated by the need for analyzing recurrent infections in a renal transplant cohort from India in which approximately 19% of patients died and over 13% had missing covariate information. Literature shows that opportunistic infections times and death time may be correlated and need to be adjusted in the estimation process. First, we studied this problem by developing methods using marginal rate models for both recurrent events and terminal events with missing data. We adopted a weighted estimating equation approach with missing data assumed to be missing at random (MAR) for estimating the parameters. Second, we considered a marginal rate model for multiple type recurrent events in the presence of a terminal event. We proposed a weighted estimating equation approach assuming that terminal events preclude further recurrent events. We adjusted for the terminal events via inverse probability survival weights. The asymptotic properties of the proposed estimators were derived using empirical process theory. Third, we extended the marginal rate model for analyzing multiple type recurrent events in the presence of a terminal event to handle missing covariates. The main goal was to examine the relationship between covariates and multiple type recurrent infections broadly classified into bacterial, fungal and viral origin from the aforementioned data. We considered a weighted estimating equation approach to estimate the parameters. Through simulations, we examined the finite sample properties of the estimators and then applied the method to the India renal transplant data for illustration in all three papers
Semiparametric Regression During 2003–2007
Semiparametric regression is a fusion between parametric regression and nonparametric regression and the title of a book that we published on the topic in early 2003. We review developments in the field during the five year period since the book was written. We find semiparametric regression to be a vibrant field with substantial involvement and activity, continual enhancement and widespread application
Hazard and Rate Regression in the Presence of Differential Selection or Termination Probability.
We study unrepresentative observational data in survival analysis.
The first paper focuses on proportional hazards regression
when observed subjects have different selection probabilities. We
develop methods which are applicable when the selection
probabilities are unknown but estimated using auxiliary information. With a two-stage method, first, a logistic model is fitted and selection probabilities are estimated. Second, a proportional hazards model is fitted to the biased sample
employing the estimated inverse selection probabilities as weights.
The asymptotic properties of the proposed estimators are derived and
evaluated in finite samples through simulation. In applying this
method to renal transplant data, the effect on transplant failure of
Expanded Criteria Donor (ECD) kidneys is estimated using the proposed
methods and found to be of considerably greater magnitude than that
implied by previous (unweighted) analyses.
In the second paper, we develop hypothesis testing
procedures for contrasting parameters from weighted and unweighted
proportional hazards models. Comprehensive statistics are proposed
for both regression parameter estimators and baseline hazard
function. Asymptotic properties of the test statistics are
derived, while the empirical significance level and power are
examined in numerical studies. Various patient characteristics are found to have
significantly different effects in transplanted patients versus
wait-list candidates.
The third paper considers recurrent events with a terminating event. The method involves fitting a proportional hazards model
for the terminating event and an additive model for the recurrent event
rate conditional on survival, then integrating over time. Two
methods are proposed to compare the mean number of events between
treatments. The first method factors out differences in the survival
distributions between treatments, while the second method features
treatment-specific survival functions. The estimators of both
proposed measures are proved to be consistent with explicit covariance functions. Asymptotic properties are evaluated in moderate-size samples and the
methods are found to be robust to unadjusted predictors. The motivating example is repeated hospitalizations after kidney transplant, where the effect of ECD
transplant compared to non-ECD transplant on the mean number of
hospitalizations is of interest. We found although ECD transplant patients tend to die sooner than non-ECD patients, they experience a significantly more hospitalizations.Ph.D.BiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/57623/2/qingpan_1.pd
Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain
The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio
Methods for non-proportional hazards in clinical trials: A systematic review
For the analysis of time-to-event data, frequently used methods such as the
log-rank test or the Cox proportional hazards model are based on the
proportional hazards assumption, which is often debatable. Although a wide
range of parametric and non-parametric methods for non-proportional hazards
(NPH) has been proposed, there is no consensus on the best approaches. To close
this gap, we conducted a systematic literature search to identify statistical
methods and software appropriate under NPH. Our literature search identified
907 abstracts, out of which we included 211 articles, mostly methodological
ones. Review articles and applications were less frequently identified. The
articles discuss effect measures, effect estimation and regression approaches,
hypothesis tests, and sample size calculation approaches, which are often
tailored to specific NPH situations. Using a unified notation, we provide an
overview of methods available. Furthermore, we derive some guidance from the
identified articles. We summarized the contents from the literature review in a
concise way in the main text and provide more detailed explanations in the
supplement (page 29)
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